Keystroke anomaly detector

design and implement a basic keystroke anomaly detector using either

Euclidean distance or Manhattan distance. Your detector will take 10 keystroke login samples per

user and generate keystroke signature for this user. When a new keystroke login sample for user X is

tested by the detector against user X keystroke signature the detector will return a value between

[0-1]. Where 0 indicate no match, 1 indicate a perfect match and any value above 0.85 is considered

a match

Attached the .csv file below

Skills: Artificial Intelligence, Python

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Project ID: #19759038

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I've read the job in question and have some prior experience with keystroke anomaly detection. I will be able to code the required solution in R or python and provide it to you as per your specifications.

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